This report contains different plots and tables that may be relevant for analysing the results. Observe:
alg1Given a problem consisting of \(m\)
subproblems with \(Y_N^s\) given for
each subproblem \(s\), we use a
filtering algorithm to find \(Y_N\)
(alg1).
Note that the width of objective \(i\), \(w_i = [l_i, u_i]\) should be approx. \(10000m\):
## # A tibble: 4 × 6
## m mean_width1 mean_width2 mean_width3 mean_width4 mean_width5
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 19255. 19233. 19224. 19012. 18703.
## 2 3 28356. 28479. 28149. 28047. 27084.
## 3 4 38029. 38259. 37875. NaN NaN
## 4 5 47502. 47820. 47251. NaN NaN
What is \(|Y_N|\) given the different methods of generating the set of nondominated points for the subproblems?
## # A tibble: 4 × 3
## method mean_card n
## <chr> <dbl> <int>
## 1 l 53640. 140
## 2 m 47876. 140
## 3 u 53710. 140
## 4 ul 49986. 140
Does \(p\) have an effect?
## # A tibble: 16 × 4
## # Groups: method [4]
## method p mean_card n
## <chr> <dbl> <dbl> <int>
## 1 l 2 2402. 40
## 2 m 2 1522. 40
## 3 u 2 598. 40
## 4 ul 2 792. 40
## 5 l 3 13578. 40
## 6 m 3 9871. 40
## 7 u 3 3471. 40
## 8 ul 3 5065. 40
## 9 l 4 75821. 30
## 10 m 4 78021. 30
## 11 u 4 78466. 30
## 12 ul 4 74500. 30
## 13 l 5 153191. 30
## 14 m 5 130213. 30
## 15 u 5 166755. 30
## 16 ul 5 150959. 30
Does \(m\) have an effect?
## # A tibble: 16 × 4
## # Groups: method [4]
## method m mean_card n
## <chr> <dbl> <dbl> <int>
## 1 l 2 14414. 80
## 2 m 2 15134. 80
## 3 u 2 14192. 80
## 4 ul 2 13406. 80
## 5 l 3 148973. 40
## 6 m 3 131004. 40
## 7 u 3 157511. 40
## 8 ul 3 145011. 40
## 9 l 4 11207. 10
## 10 m 4 7464. 10
## 11 u 4 2700. 10
## 12 ul 4 4666. 10
## 13 l 5 28539. 10
## 14 m 5 17719. 10
## 15 u 5 5657. 10
## 16 ul 5 7844. 10
We classify the nondominated points into, extreme, supported non-extreme and unsupported.
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